Compară metode

	Propagarea etichetelor	Pădurea Aleatoare (Random Forest)	Clustering Spectral
Domeniu	Învățare automată	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning	Machine learning
Anul apariției	2002	2001	2002
Autorul original	Zhu, X. & Ghahramani, Z.	Breiman, L.	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Tip	Graph-based semi-supervised classification	Ensemble (bagging of decision trees)	Graph-based clustering (spectral method)
Sursa seminală	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14, 849–856.
Denumiri alternative	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Înrudite	3	4	5
Rezumat	Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	Spectral Clustering is a graph-based unsupervised learning algorithm, formalized by Ng, Jordan, and Weiss in 2002, that maps data points into a low-dimensional eigenspace derived from the similarity graph's Laplacian before applying k-means. This spectral embedding makes it possible to recover clusters of arbitrary shape — rings, crescents, interleaved spirals — that Euclidean distance-based methods consistently fail to separate.